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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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Updated: Sep 10, 2025

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Analysis of anti-cancer treatment therapies based on TOPSIS method under fractional Diophantine fuzzy Muirhead mean

Abbas Qadir1, Saleem Abdullah1, Ariana Abdul Rahimzai2

  • 1Department of Mathematics, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.

Scientific Reports
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using fractional Diophantine fuzzy sets (FDFSs) and analytical hierarchy process (AHP) to select optimal, affordable cancer treatments, improving medical decision-making.

Keywords:
Analytical hierarchical processFractional Diophantine fuzzy setMuirhead mean operatorsTOPSIS method

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Area of Science:

  • Decision Sciences
  • Medical Informatics
  • Applied Mathematics

Background:

  • Selecting optimal cancer treatments is complex due to numerous factors and uncertainties.
  • Existing decision-making methods may not adequately address hidden weight information for criteria and experts.

Purpose of the Study:

  • To develop a novel methodology for selecting the most appropriate and affordable cancer treatments.
  • To integrate fractional Diophantine fuzzy sets (FDFSs) and the analytical hierarchy process (AHP) for enhanced medical decision support.

Main Methods:

  • Introduced fractional Diophantine fuzzy sets (FDFSs) and the Muirhead mean operator to handle uncertainty.
  • Utilized the analytical hierarchy process (AHP) to determine weights of criteria and decision-makers.
  • Proposed and applied a fractional Diophantine fuzzy-TOPSIS (FDF-TOPSIS) technique for treatment evaluation.

Main Results:

  • The FDF-TOPSIS technique was successfully applied to a real-world cancer treatment selection problem.
  • The proposed method demonstrated reliability, accuracy, and feasibility in comparison to existing approaches.

Conclusions:

  • The developed FDF-TOPSIS method offers a robust framework for cancer treatment selection.
  • This approach has the potential to significantly improve the decision-making process for healthcare professionals in oncology.